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VISUAL RELATIONSHIP CLASSIFICATION WITH NEGATIVE-SAMPLE MINING

Citation Author(s):
Roberto de M. Estevão Filho, José Gabriel R. C. Gomes, Leonardo de O. Nunes
Submitted by:
Roberto Estevao
Last updated:
8 December 2020 - 8:11am
Document Type:
Presentation Slides
Document Year:
2020
Event:
Presenters Name:
Roberto de Moura Estevão Filho
Paper Code:
ARS-18.11

Abstract 

Abstract: 

This paper introduces the application of a visual relationship classifier as a standalone system that is meant to be used with external detectors. Through these lens, we propose a training scheme that uses unannotated pairs of objects as negative samples in order to improve precision. The proposed network architecture incorporates common techniques presented in related state-of-the-art solutions with a novel positional encoding scheme. We evaluate the proposed training method and architecture on the Open Images dataset and improve mAP from 34.6% to 78.2% when considering all possible object pairings in each image. For a case where only ground-truth pairs are considered, our method presents a small decrease, from 91.0% to 88.8% mAP.

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